log in to your account

welcome back! please log in to continue.

Altivon

What’s Love Got To Do With [AI]

August 20, 2018


Sure, there’s a lot of hype about contact center artificial intelligence (AI). Tom Jones or John Paul Young would agree “love is in the air” when so many individuals’ blogs, corporate websites, and industry conferences rave over AI. However, the standard rules of business apply, which puts this hot topic firmly in Tina Turner’s realm: “What’s Love Got to Do with AI?”

In one Harvard Business Review (HBR) article, “Most of AI’s Business Uses Will Be in Two Areas”, authors cite one popular rule: ‘Follow the Money’. The authors follow and explain how AI will find love and money in Supply Chain Management and “functions taking advantage of traditional analytics techniques”. For our contact center industry, techniques such as Deep Learning, a subset of AI (see image), will help us to find those hidden correlations and discard coincidences, so our collective time and talents can be better deployed on the human-driven Customer Experience (CX).

What’s Love But a Sweet Old Fashioned Notion


We’ve got enhanced ways to achieve the 1980’s old-fashioned notion of Better, Faster, Cheaper. In a second HBR article, “Collaborative Intelligence: Humans and AI Are Joining Forces”, authors target three specific ways for humans and AI to vastly improve CX: (1) “amplify our cognitive strengths; (2) interact with customers and employees to free us for high-level tasks; and (3) embody human skills to extend our physical capabilities“.

Amplify our cognitive strengths

Imagine Agent Susie at her desk in a sales call center. Headset in place, login at the ready, and eagerly awaiting her first customer call. Why ‘eagerly’? Susie is a ‘people person’ and doesn’t interface well with technology. Type an address here, insert comments there and there, follow the script!

Let Susie be the customer’s interface, ask critical questions about wants and needs, while AI partnerships like Google’s and Genesys’ with natural language understanding (NLU) could search for product matches, simultaneously push / screen-pop images in a co-browse session to Susie and her caller, which can leverage machine learning or even speech analytics to assess excitement and address requirements of product color, size, and delivery preferences.

Interact with customers and employees to free us for high-level tasks

Amazon’s Alexa, Apple’s Siri, Google’s (nameless) Assistant and Microsoft’s Cortana on our smartdevices serve us—another sweet, old-fashioned notion. We speak and list a number of requests—record notes, send a meeting invite, order products or services with “skills”, report a utility outage with NLU, browse shows on the TV, and even trigger a complicated task sequence—to reduce keyboard-driven carpal tunnel syndrome.

Embody human skills to extend our physical capabilities

The authors’ give an example of AI-enabled machines called cobots, where humans interact with heavy machinery via exoskeletons on assembly lines like at Hyundai and Mercedes-Benz may not directly apply to our contact centers’ agents and customers. However, cobots’ sensors in the field could better inform our agents of changes.

For example, there may come a time when utility linemen operate beside, or inside, cobots to safely and quickly repair broken power or gas lines. With Internet of Things (IoT) technology, AI algorithms may predict when service will be restored.

In addition to these three collaborative ways, the authors identified five characteristics that “companies typically want to improve: flexibility, speed, scale, decision making, and personalization”. Read Genesys’ CX Report for industry comparisons, mobile-first and segmentation strategies, customized service, and potentially new revenue opportunities with statistics to support!

What’s Love But a Second Hand Emotion

For any of those five characteristics to succeed, a third HBR article, “How to Make an AI Project More Likely to Succeed”, can inspire our efforts and serve to reinforce the previous HBR article, “Collaborative Intelligence”. To truly deliver, we need a CX-focused plan to achieve our AI initiative(s), not just a second hand emotion.

  1. Make your purpose clear. AI exists in the context of a company’s business model, process, and culture.
  2. Wisely choose tasks to automate. As an example, author explains how Apple chooses to automate background tasks, so their personnel can interact with customers.
  3. Choose data wisely. Think about how a company’s data may influence or bias what behaviors to model.
  4. Shift humans to higher-value social tasks. As a result of machine learning (ML) to sift through data and describe / depict the customer’s journey, the author concludes that “we will increasingly see a shift in value from cognitive skills to social skills”.

So, What’s Love Got To Do With AI?

In the end, there will absolutely be plenty of “love got to do with it”, but common business approaches will drive our efforts forward. Whether a company chooses to analytically “follow the money”; embrace an approach to “amplify, interact, embody”; or build a project plan to get there, we must keep CX at the heart of these initiatives.